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Robust Object Detection

A Benchmark for the: Robustness of Object Detection Models to Image Corruptions and Distortions

To allow fair comparison of robustness enhancing methods all models have to use a standard ResNet50 backbone because performance strongly scales with backbone capacity. If requested an unrestricted category can be added later.

Benchmark Homepage: https://github.com/bethgelab/robust-detection-benchmark

Metrics:

mPC [AP]: Mean Performance under Corruption [measured in AP]

rPC [%]: Relative Performance under Corruption [measured in %]

Test sets: Coco: val 2017; Pascal VOC: test 2007; Cityscapes: val;

( Image credit: Benchmarking Robustness in Object Detection )

Papers

Showing 7180 of 90 papers

TitleStatusHype
Towards Robust Object Detection: Bayesian RetinaNet for Homoscedastic Aleatoric Uncertainty Modeling0
Scene-aware Learning Network for Radar Object Detection0
A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection0
Multi-Target Domain Adaptation via Unsupervised Domain Classification for Weather Invariant Object Detection0
Labels Are Not Perfect: Improving Probabilistic Object Detection via Label Uncertainty0
Exploring Thermal Images for Object Detection in Underexposure Regions for Autonomous Driving0
Robust Object Detection under Occlusion with Context-Aware CompositionalNets0
Proposal Learning for Semi-Supervised Object Detection0
Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement ApproachCode0
Towards Adversarially Robust Object Detection0
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